Method and apparatus for determining sleep states

ABSTRACT

An apparatus is provided for detecting Macro Sleep Architecture states of a subject such as WAKE, NREM and REM sleep from a subject&#39;s EEG. The apparatus includes an EEG digital signal assembly of modules arranged to convert analogue EEG signals into digital EEG signals. A bispectrum assembly is responsive to the EEG digital signal assembly and converts the digital EEG signals into signals representing corresponding bispectrum values. A bispectrum time series assembly, in electrical communication with an output side of the bispectrum assembly, generates at least one bispectrum time series for a predetermined frequency. A macro-sleep architecture (MSA) assembly is responsive to the bispectrum time series assembly and is arranged to produce classification signals indicating classification of segments of the EEG signals into macro-sleep states of the subject.

FIELD OF THE INVENTION

The present invention is concerned with a method and apparatus forautomatically determining states of sleep of a subject. Embodiments ofthe invention find particular application in aiding the diagnosis ofsleep disorders and assessing daytime sleepiness.

1. Background

The reference to the prior art in the following discussion is not to betaken as any representation or admission that such art forms part of thecommon general knowledge. The disclosures of each of the publicationsreferred to herein are hereby incorporated by reference in theirentireties and for all purposes.

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious sleepdisorder with high prevalence among the population [1, 2]. In the USA,about 24% of men and 9% of women fall within Medicare guidelines [1] fortreatment. In Singapore 15% of the total population is at risk [3]. Over1.2 million Australians experience sleep disorders costing the country$10.3 billion (in 2004) [4]; OSAHS is the commonest disorder (66% of thetotal).

OSAHS is characterized by breathing interruption during sleep. Fullclosure of the airways is known as obstructive Apnea, and a partialclosure is defined as obstructive Hypopnea (See Appendix A for technicaldefinitions). The common symptoms of OSAHS are excessive daytimesleepiness and intermittent snoring [5, 6].

OSAHS is a major risk factor for downstream complications such asstroke, diabetes and cardiovascular disease [6, 7]. It is also known tobe associated with cognitive deficiencies, low IQ in children, fatigueand accidents. It is responsible [7] for 11,000-43,000 traffic accidentsper year in NSW. Untreated patients are known to utilize twice thenational health resources prior to diagnosis [8]. OSAHS is treatable. Ifdiagnosed early, its devastating secondary complications can bethwarted. However, over 90% individuals with OSAHS are estimated to beundiagnosed at present [2].

The standard test for OSAHS diagnosis is Polysomnography (PSG) [9]. PSGis a technique to monitor multiple neuro-physiological and cardiorespiratory signals, over the course of night. It requires a full-nightsleep-laboratory stay in a specifically equipped sleep-suite, connectedto over 15 channels of measurements. The 6-8 hours of sleep data is thensubjected to a complex and time-consuming manual process (Sleep Scoring)to identify events of Apnea/Hypopnea and a type of sleep a disturbanceknown as EEG-arousals (EEGA). The outcomes of PSG test are summarymeasures of OSAHS severity such as the Respiratory Disturbance Index(RDI) and the EEG Arousal index (ArI) etc (please see appendix B fordetails).

1.1 EEG in the Diagnosis of Sleep Disorders 1.11 Sleep Scoring andMacro-Sleep-Architecture

Sleep is essentially a neuropsychological phenomenon; EEG still remainsthe cheapest and the most portable technique for the functional imagingof the brain during sleep. It is also the technique with the highesttemporal resolution available. In the current practice of PSG Scoring,EEG is regarded as an indispensable signal when a definitive diagnosisis desired. Thus, in-facility diagnostic PSG tests always include EEG.Electromyography (EMG) and Electroocculography (EOG) signals are alsoneeded for the correct EEG-centred interpretation of sleep states.

In diagnostic PSG tests EEGs are essential for the following tasks:

-   -   (i) to define EEG-arousals and identify sleep fragmentation.        EEG-arousals are also used as one parameter in defining        Hypopneas (see Appendix A).    -   (ii) to score the Macro Sleep Architecture (MSA) of sleep. It is        a process in which sleep is classified into three macro        states: (1) Wake State (S^(W)), (2) Rapid Eye Movement (REM)        Sleep State (S^(R)), and (3) Non-REM Sleep State (S^(N)). The        MSA is extremely important in the diagnosis of OSAH. In        addition, Sleep MSA may be used in the diagnosis/monitoring of a        range of sleep disorders including Narcolepsy, Insomnia, Sudden        Infant Death Syndrome and Depression etc.

Some important uses of MSA in PSG includes:

-   -   (a) Estimating the Total Sleep Time, TST, which is needed for        the computation of the RDI index. The TST is also useful as a        summary indicator of the quality of sleep during a PSG test.        Note that the TST, which is defined using EEG, can be        significantly different from the Total Time in Bed (TTB)        measured with a clock.    -   (b) Estimating clinically important descriptors of sleep such as        the Sleep Efficiency (SE), Sleep Latency (SL), REM Latency        (RSL), the total time spent in REM, and the percentage of time        spent in REM.    -   (c) Expressing most of the clinically relevant sleep parameters        separately for REM and NREM sleep, before providing an overall        number. Some Examples are: the Arousal Index in REM sleep, The        Arousal Index in NREM sleep, RDI in REM sleep and RDI index in        NREM sleep. The reason behind this is that the REM/NREM        classification provides fundamental information about sleep and        its diagnostic characteristics.

An EEG may be broadly divided into four major frequency bands [10],Delta (δ, 0.1-4 Hz), Theta (θ, 4.1-8 Hz), Alpha (α, 8.1-12 Hz), and Beta(β, >12.1 Hz). FIG. 1 shows the EEG activity at different states, (a)awake drowsy state, (b) light sleep (NREM sleep Stage 1 and Stage 2),(c) deep sleep (NREM sleep Stage 3 and Stage 4) and (d) REM sleep. Thesefrequency bands are heavily used in sleep scoring.

The scoring of MSA is done manually using the rules laid down byRechtschaffen and Kales (R&K, 1968) (see appendix C for a summary) [11].Manual scoring relies on visual extraction of specific features in twoEEG channels (usually C3-A2 and C4-A1 of the International 10/20system), two channels each of EMG and EOG. Thus, six channels ofelectrophysiological data have to be visually interpreted,simultaneously taking care of difficulties such as measurementartifacts.

This process is time consuming (typically 1-2 hours per patient), costly(hundred of dollar per recording) and prone to inter and intra scorervariability [12-15]. The scorers from different laboratories tend toagree less than scorers from the same laboratories, due to differencesin interpretation and subjective implementations. For example, the meanepoch by epoch agreement between the scorers from three sleeplaboratories in the USA for healthy subjects is 76% (range 65-85%) whichdecreases to 71% (range 65-78%) in the OSAHS cases [16]. A similarresult (76.8%) has been reported by European laboratories based on alarge database of 196 recordings from 98 patients [13].

The accurate computation of sleep parameters such as the RDI, ArI andREM Latency is important for the clinical diagnosis of a range of sleepdisorders. Thus, the final diagnostic accuracy heavily depends on theprecise scoring of MSA. However, due to the subjectivity associated withthe scoring process there exists a significant variability in the PSGresults between the technicians of the same laboratory and across thedifferent sleep laboratories [14, 15]. For example, [12] reported that apatient can get two different diagnoses in two different laboratorieswhich might range from as low as RDI=4.9 to as high as RDI=79.

In order to overcome the problems associated with manual scoring andcater to the ever increasing demand for PSG testing, several researchershave proposed automatic sleep scoring systems [17-22]. After publicationof R&K's rules in 1968, several authors tried to automate them andachieved various degrees of agreement with human scorers. With theadvancement in digital signal processing techniques, several other usedfrequency spectral analysis [22], neural network analysis [18, 20],multidimensional scaling and wavelets techniques or expert systemapproaches [21] to develop automatic sleep staging systems. However, areliable and accurate method with sufficient precision suitable forin-facility PSG as well as other take-home OSAHS screening devices doesnot exist yet. Despite the inherent subjective nature, human scoring isstill considered the golden method of MSA scoring. Existing methods forautomatic MSA scoring have the following shortcomings:

-   -   1. The R&K rules depend on visual features in sleep EEG and were        originally proposed specifically for manual scoring. Most of the        automated techniques try to implement R&K rules and depend on        morphological features like k-complexes, vertex-waves and        spindles. These characteristics are severely altered in disease        states such as OSAHS [6, 10, 23, 24]. Consequently their        performances decreases in OSAHS [19, 25]. In addition, the        detection of visual features is a highly subjective process.    -   2. The agreement between automatic and human classifications is        smaller than the agreement between human scorers [25]. This        result is to be expected because the R&K criteria are based on        visual features and humans are better than machines in visual        pattern recognition.    -   3. The differentiation of REM/NREM/WAKE is critical in OSAHS        diagnosis. However, automated methods had difficulties in        distinguishing wake state from Stage 1 of NREM sleep and REM        sleep.    -   4. Automated scoring techniques currently available in PSG        equipment need expert human intervention for manually editing        the outcomes, and thus are not truly automated systems [25]. The        human intervention makes them subjective and time consuming.    -   5. Existing methods have not been tested under disease        conditions such as OSAHS, Periodic Leg Movement Syndrome (PLMS)        or upper airway respiratory syndrome (UARS), where sleep is        corrupted with frequent EEG arousals, apnea events, and        recording artifacts.    -   6. All existing techniques depend on recording multiple        physiological signals, making them unsuitable for portable        monitors used for OSAHS screening.

The AASM definition [26] of micro-sleep is: “ . . . an episode lastingup to 30 seconds during which external stimuli are not perceived. ThePSG suddenly shifts from waking characteristics to sleep”. It isgenerally believed that micro-sleep is closely associated with excessivediurnal sleepiness. Excessive daytime sleepiness and spontaneousmicro-sleep are two major consequences of OSAHS [27], contributing tomotor vehicle and work related accidents. It is estimated to affect 12%of the adult population [27].

Clinically, sleepiness is commonly expressed by the measure SleepLatency (SL), which is the length of time required to fall asleep. Thecommon tests for measuring SL are Multiple Sleep Latency Test (MSLT) andMaintenance of Wakefulness Test (MWT), technical details for which areprovided in Appendix E. In these tests SL is computed as the time fromthe start of recording to the sleep onset. To technically identify sleeponset, sleep technician have to simultaneously look at multiple signals.It is a tedious and a subjective process resulting in high inter-rater,as well as intra-rater, variability [12]. The SL also provides valuableinformation in the diagnosis of other widespread diseases such asinsomnia.

Even though diurnal micro-sleep is an important phenomenon related tosleep disturbances, there is no objective system of measurements todetect micro-sleep or express its severity. In routine PSG teststargeted for OSAHS diagnosis, episodes of micro-sleeps are not scored.

It is an object of the present invention to provide a method andapparatus that addresses one or more of the various problems discussedabove in relation to prior art methods for determining sleep-relatedparameters.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention there is provided amethod of determining sleep states from an EEG signal of a subject, themethod comprising the steps of:

-   -   electronically processing the EEG signal to generate a third or        higher order spectrum of said signal;    -   electronically processing said spectrum to produce at least one        spectrum time series for a predetermined frequency; and    -   electronically processing said spectrum time series for        compliance with predetermined criteria to tangibly classify        segments of the EEG signal as corresponding to particular        macro-sleep states of the subject.

In one embodiment the segments of the EEG signal are tangibly classifiedby indicating their classification on an electronic display for viewingby a user.

Preferably the step of electronically processing the spectrum to produceat least one spectrum time series produces a first spectrum time seriesand a second spectrum time series for corresponding first and secondfrequencies.

Preferably the spectrum comprises a bispectrum and the spectrum timeseries comprises a bispectrum time series.

The step of electronically processing said bispectrum time series forcompliance with predetermined criteria will preferably includeprocessing the first bispectrum time series to classify a correspondingone of said segments as Wake or Sleep.

In a preferred embodiment the method involves a step of electronicallyprocessing the second bispectrum slice time series of said segmentclassified as Sleep to further classify said segment as NREM or REMsleep.

Preferably the step of electronically processing the bispectrum toproduce at least one bispectrum time series produces a first bispectrumtime series and a second bispectrum time series for corresponding firstand second frequencies.

The step of electronically processing said bispectrum time seriesagainst predetermined criteria will preferably include processing thefirst bispectrum time series to classify a corresponding one of saidsegments as Wake or Sleep.

In a preferred embodiment the method involves a step of electronicallyprocessing the second bispectrum time series of said segment classifiedas Sleep to further classify said segment as NREM or REM sleep.

According to the preferred embodiment, the first frequency falls withina range of 1-8 Hz and the second frequency is greater than 9 Hz.

The method may include a step of electronically smoothing the secondbispectrum time series prior to processing said second bispectrum timeseries against predetermined criteria.

The step of electronically processing the first bispectrum time seriesagainst predetermined criteria will preferably include comparing valuesof a segment of the first bispectrum time series to a threshold valuecomputed from the first bispectrum time series.

Preferably the step of electronically processing the second bispectrumtime series against predetermined criteria includes comparing values ofa segment of the second bispectrum time series to a threshold valuecomputed from the second bispectrum time series.

The method may include a step of indicating the particular macro-sleepstates of the subject with an electronic display or another sensorysignal modality such as a sound or tactile indication.

Preferably the step of generating a bispectrum by electronicallyprocessing the EEG signal comprises an indirect estimation method

The indirect method may include applying a Fourier transform to cumulantvalues corresponding to the EEG signal.

Alternatively, the step of generating a bispectrum by electronicallyprocessing the EEG signal may comprise a direct estimation method.

The method may include a step of electronically processing the firstbispectrum time series to produce an index indicating sleepiness of thesubject.

Preferably the step of electronically processing the first bispectrumtime series to produce said index comprises determining a fraction oftime over a predetermined period that said series approaches apredetermined threshold value.

According to a further aspect of the present invention there is providedan apparatus for detecting sleep states of a subject including:

-   -   an EEG digital signal assembly of modules arranged to convert        analogue EEG signals into digital EEG signals;    -   a spectrum assembly responsive to the EEG digital signal        assembly and arranged to convert the digital EEG signals into        signals representing spectrum values;    -   a spectrum time series assembly in electrical communication with        an output side of the spectrum assembly and arranged to generate        at least one spectrum time series for a predetermined frequency;        and    -   a macro-sleep architecture (MSA) assembly responsive to the        spectrum time series assembly and arranged to produce        classification signals indicating classification of segments of        the EEG signals into macro-sleep states of the subject.

Preferably the spectrum assembly is a bispectrum assembly that isarranged to convert the digital EEG signals into signals representingbispectrum values. Alternatively, other higher order spectrum assembliesmight also be used, such as a trispectrum.

Preferably the spectrum time series assembly comprises a bispectrum timeseries assembly arranged to generate at least one bispectrum time seriesfor a predetermined frequency.

In one embodiment, the macro-sleep architecture (MSA) assembly isresponsive to the bispectrum time series assembly.

Preferably the EEG digital signal assembly includes:

-   -   one or more EEG electrode ports;    -   an analogue signal conditioning module in electrical        communication with the EEG electrode ports;    -   an analogue to digital converter in electrical communication        with the analogue signal conditioning module; and    -   a bandpass digital filter arranged to process output from the        analogue to digital converter to produce the digital EEG        signals.

Preferably the bispectrum assembly comprises:

-   -   a cumulant calculator;    -   a Fourier transform module responsive to the cumulant calculator        to produce a signal representing values of a bispectogram; and    -   a bispectrum time series estimator arranged to produce at least        one bispectrum time series signal representing values at a        predetermined frequency of a slice of the bispectogram.

The MSA assembly preferably includes a comparator to determine if thevalue of the at least one bispectrum time series signal exceeds apredetermined value over a segment.

Preferably the apparatus includes a sleepiness index calculatorresponsive to the bispectrum time series estimator and arranged togenerate a signal indicating sleepiness of the subject based on thebispectrum time series signal for the predetermined frequency fallingwithin the EEG Delta band.

In a preferred embodiment the apparatus has a display to display a macrosleep status determined by the MSA.

The user interface may include a display in communication with thesleepiness index calculator to display the sleepiness index.

Preferably the apparatus is arranged as a driver sleep managementsystem, wherein the sleepiness index calculator is incorporated into asleepiness index alert unit, said alert unit controlling one or more of:

-   -   a driver intervention module for increasing the wakefulness of        the driver;    -   a wireless communications module for communicating with a base        station;    -   a vehicle control interface for appropriately and safely        immobilising the vehicle; and    -   a data logger for recording sleepiness index values of the        driver.

According to a further aspect of the present invention there is provideda media readable by machine, tangibly embodying a program ofinstructions executable by the machine to cause the machine to performthe previously described method to determine sleep states from an EEG ofa subject.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred features, embodiments and variations of the invention may bediscerned from the following Detailed Description which providessufficient information for those skilled in the art to perform theinvention. The Detailed Description is not to be regarded as limitingthe scope of the preceding Summary of the Invention in any way. TheDetailed Description will make reference to a number of drawingsthroughout as follows:

FIG. 1A is a graph of EEG, EOG and EMG activity of an awake and drowsysubject exhibiting EEG activity of 5-8 Hz, slow eye movement andvoluntary EMG activity.

FIG. 1B is a graph of EEG, EOG and EMG activity of the subject in NREMstage 2 sleep with EEG showing features such as k-complex and spindles.

FIG. 1C is a graph of EEG, EOG and EMG activity of the subject in NREMstage 3 and 4 sleep, with EEG showing high delta activity (1-3 Hz).

FIG. 1D is a graph of EEG, EOG and EMG activity of the subject in REMsleep with EEG activity between theta and alpha range exhibitingintermittent eye movement and suppressed EMG activity.

FIG. 2 is a flowchart of a method for estimating macro sleeparchitecture states of a subject according to an embodiment of thepresent invention.

FIG. 2A is a plan view of a human head indicating electrode placementpositions.

FIG. 3 is a graph of bispectrum magnitude computed from EEG segmentsduring different macro sleep states, namely Waking (S^(R)), NREM (S^(N))and REM (S^(R)) from which it may be observed that during wake and REMsleep a low amplitude is present with several peaks to be seen atdifferent frequencies, whereas during NREM sleep only a single peak canbe seen concentrated around ω<5 Hz.

FIG. 4 is a graph illustrating a correspondence betweenslice-bispectogram and sleep states of a subject.

FIG. 5 shows the effect of pre-processing (smoothing, de-trending andequalization) applied to EEG Bispectrum Time Series (BTS).

FIG. 6 compares graphs of bispectrum magnitudes at 2 Hz and 20 Hz withtechnician scored macro sleep states.

FIG. 7 is a flowchart of a method for determining MSA from twobispectrum time series according to a preferred embodiment of thepresent invention.

FIG. 8A is a graph illustrating variation in accuracy for each of WakeNREM and REM states as a function of D in minutes.

FIG. 9 illustrates PSG scored sleep states compared with automatedestimated sleep states determined by a method according to an embodimentof the invention.

FIG. 10 illustrates PSG scored sleep states compared with automatedestimated sleep states determined by a further embodiment of theinvention.

FIG. 11 comprises a number of scatter plots showing the relationshipbetween the parameters Total Sleep Time (TST), Sleep Efficiency (SE),REM Sleep Latency (RSL) and percentage sleep computed using TSSS andHESS.

FIG. 12 shows Altman-Bland plots for TST, SE, RSL and percentage sleepand illustrates good agreement between TSSS and HESS in estimating MSAparameters.

FIG. 13A is a graph illustrating the results of a search for optimumthreshold values used in a method according to an embodiment of thepresent invention.

FIG. 13B is a further graph illustrating the results of a search foroptimum threshold values used in a method according to an embodiment ofthe present invention.

FIG. 14 shows the estimated sleep states using a method according topreferred embodiment of the present invention (herein referred to asHESS) using EEG data from the electrode locations Fp2, F3, Fz and P3over the scalp.

FIG. 15 compares a subject's bispectrum time series to the subjectstechnician scored sleep stages and indicates micro-sleep events.

FIG. 16 is a graph of physiological parameters of the subject recordedover a brief time interval of the graph of FIG. 15.

FIG. 17 comprises two graphs comparing sleepiness index (SI), uppergraph, determined according to an embodiment of the present inventionwith technician scored sleep states, lower graph, for a first nap.

FIG. 18 comprises two graphs comparing sleepiness index (SI), uppergraph, determined according to an embodiment of the present inventionwith technician scored sleep states, lower graph, for a second nap.

FIG. 19 is a block diagram of an apparatus for detecting and indicatingsleep states according to an embodiment of the present invention.

FIG. 20 is a diagram showing the apparatus of FIG. 19 in use.

FIG. 21 is a block diagram of an apparatus for detecting sleepiness of asubject in a vehicle according to a further embodiment of the presentinvention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 2. Method 2.1 EEG DataAcquisition

The clinical data acquisition environment for this work is the SleepDiagnostic Laboratory of The Prince Alexandra Hospital, Australia. Thedata was recorded using clinical Polysomnography (PSG) equipment(Siesta, Compumedics®, Sydney, Australia). Patient preparation,electrode placement and instrumental set-up were done by an experiencedsleep technician according to AASM guidelines [9]. Table 3 describes thedemographic details of the subjects studied.

Database A: From each subject in this database, routine PSG data wascollected. In a typical PSG test, signals such as ECG, EEG, EMG, EOG,nasal/oral airflow, respiratory effort, body positions, body movementsand blood oxygen saturation are monitored. EEG data was recorded fromboth hemispheres using electrode positions C4, C3, A2, and A1, based onthe standard international 10-20 system of electrode placement [10]illustrated in FIG. 2A. The subject population includes individuals withsymptoms such as daytime sleepiness, snoring, tiredness, lethargy etcand are suspected of sleep apnoea. They were referred to the hospitalfor a routine PSG test.

Database B: In this database, EEG data from 19 different positions (seeFIG. 13) on the skull were recorded in addition to the conventional PSGchannels described under Database A above. Electrode locations were asspecified in the Standard 10-20 system of EEG electrode placement [10].The data recorded in this database were from healthy volunteers, definedas individuals without any symptoms of the OSAHS syndrome.

Database C: This database contains data from the subjects referred forthe Multiple Sleep Latency Test (MSLT) [28] or Maintenance ofWakefulness Test (MWT) [6]. Typical physiological signals which arerecorded in MSLT and MWT are (i) 4 channels of EEG (C3, C4, O1, O2 withreference to A1 and A2). (ii) left and right eye Electro-oculograms(EOGs), and (iii) submentalis electromyogram (EMG).

The collected data was segmented into sub-records of length M samplesfor further analysis. Note: In the field of sleep medicine thesesegments are conventionally termed as epochs. So from here onward theywill be referred to interchangeably as epochs or segments.

2.2 HOS-Based Analysis of EEG

Let x^(i) (k), i=1, 2, 3 . . . , N, denote the k-th sample of the i-thsegment of digitized EEG data where N is the total number of segments ina recording. The samples x^(i)(k) are modelled as:

x ^(i)(k)=h ^(i)(k)=e ^(i)(k)+w ^(i)(k)  (1)

where e^(i)(k) is a white non-Gaussian process, and h^(i)(k) is astable, possibly non-minimum phase kernel representing the underlyingsystem generating the EEG segment x^(i)(k). The term w^(i)(k) representsmeasurement noise within the frequency band of interest, which istraditionally modelled as a white Gaussian process. The inventors haverelaxed this constraint and allow the measurement noise to be either awhite or colored Gaussian noise, or, any noise process with asymmetrical probability density function.

A method according to an embodiment of the present invention will now bedescribed that discounts noise processes e^(i)(k) and w^(i)(k) andcapture the features of the underlying EEG generating mechanism via thekernel h^(i)(k) thereby enabling classification of EEG intoREM/NREM/WAKE stages, as well as enable us to define a measure ofsleepiness. The inventors achieve that in the 3^(rd)-order spectradomain in order to keep the phase information in h^(i)(k) intact. Whilethe third order spectra is preferred by the inventors and animplementation discussed on the third order spectra is described indetail herein other spectra of order greater than two may also be used.For example the fourth order spectrum, i.e. the trispectrum might beemployed.

2.21 The Bispectrum Estimation

The third order spectrum of a signal is known as the bispectrum. Thebispectrum can be estimated as the 2D Fourier transform of the thirdorder cumulant sequence C^(xi)(τ1,τ2) [29]. Transforming (1) to theCumulant domain, we obtain:

C ^(xi)(τ1,τ2)=E{x ^(i)(k)·x ^(i)(k+τ1)·x ^(i)(k+τ2)}  (2)

where the sequence x^(i)(k) is assumed to be zero mean. (If thiscondition is not satisfied, we can easily subtract the mean of thesignal to make it zero mean). The bispectrum B^(xi)(ω1,ω2) of x^(i)(k)is given by,

B ^(xi)(ω1,ω2)=γ·H(ω1)·H(ω2)·H*(ω1+ω2)+B ^(wi)(ω1+ω2)  (3)

where H(•) is the one dimensional Fourier transform of x^(i)(k), γ isthe skewness of e^(i)(k), and B^(wi)(ω,ω2) is the bispectrum of thenoise process wi. Since the bispectrum of a Gaussian process is zero,B^(wi)(ω,ω2)=0, leaving a high SNR signal:

B ^(xi)(ω1,ω2)=γ·H(ω1)·H(ω2)·H*(ω1+ω2)  (4)

which depends on the EEG system response h^(i)(k).

The bispectrum obtained using (4) will be a complex number. Unlike thepower spectrum (2^(nd) order statistics) based on the autocorrelation,bispectrum preserves Fourier phase information. Thus, the inventors haveconceived that the EEG System Response h^(i)(k) can be estimated fromthe bispectrum keeping the true Fourier phase intact. In contrast, powerspectrum (or autocorrelation based) techniques lose phase information,and the EEG System Response estimated from it will be the minimum-phaseequivalent of the original response.

Equation (4) points out that B^(xi)(ω1,ω2) carries information on theEEG system response which, the inventors have realised, providediagnostic features to score sleep. Note that the response H(ω) appearsin (4) as a non-linearly transformed quantity. B^(xi)(ω1,ω2) is amulti-dimensional signal, and using it to derive features for stagingsleep can be difficult.

In [30] the problem of signal reconstruction from the bispectrum wasconsidered, and it was proved that any 1-dimensional slice of thebispectrum carries sufficient information to estimate a system responsewithin a time-shift, as long as the chosen slice is not parallel to anyone of the frequency axes or to the diagonal at 135 degrees. Theinventors have understood that such a result may be utilized, in apreferred embodiment of the invention, to reduce the computationalcomplexity of the HOS techniques and to identify features easily forsleep staging. The flexibility offered by the choosing of arbitrarilyoriented and shifted oblique slices is also advantageous in avoidingunfavourable regions in the Bispectrum.

In the frequency domain, a quantity P_(i)(ω;φ,ρ) can be defined for eachdata segment x_(i)(k) such that P_(i)(ω;φ,ρ)=B^(xi)(ω, φω+ρ) describes aone-dimensional slice inclined to the ω₁-axis at an angle tan⁻¹φ andshifted from the origin along the ω₂-axis by the amount ρ, (−π<ρ<π)[30].

2.22 The Slice-Bispectrogram and the Bispectrgram Time Series (BTS)

The slice P_(i)(ω;φ,ρ) carries complete information on the EEG systemresponse (i.e. the underlying EEG generating system) according to themodel we have adopted. Thus, in order to describe the overnight data ina graphical way, we define a matrix S_(B)(ω;φ,ρ) such that:

S _(B)(ω;φ,ρ)=[P ₀(ω;φ,ρ)|P ₁(ω;φ,ρ)| . . . P _(i)(ω;φ,ρ), . . . P_(N−1)(ω;φ,ρ)]  (5)

where the i^(th) column of S_(B) represents a vector [P_(i)(ω;φ,ρ),−π<ω<π]^(T). We call this matrix the Slice-Bispectrogram. When displayedas an image, it illustrates the time evolution of the slice P_(i)(ω;φ,ρ)during the course of the night.

The inventors have conceived that features to stage sleep intoREM/NREM/WAKE stages can be derived from S_(B)(ω;φ,ρ). A set of timesseries, called Bispectrum-Time-Series (BTS, ξ_(f)), is formed byconsidering a set of fixed ω, i.e. ω=ω₀, ω=ω₁, ω₂ . . . ω_(N−1) asfollows:

$\begin{matrix}{{{{{Time}\mspace{14mu} {series}\mspace{14mu} 0\left( \xi_{0} \right)}:{S_{B}\left( {{\omega_{0};\varphi},\rho} \right)}} = \begin{Bmatrix}{{P_{0}\left( {{\omega_{0};\varphi},\rho} \right)},{P_{1}\left( {{\omega_{0};\varphi},\rho} \right)},\ldots \mspace{14mu},} \\{{P_{i}\left( {{\omega_{0};\varphi},\rho} \right)},\ldots \mspace{14mu},{P_{N - 1}\left( {{\omega_{0};\varphi},\rho} \right)}}\end{Bmatrix}},} \\{{{{{Time}\mspace{14mu} {series}\mspace{14mu} 1\left( \xi_{1} \right)}:{S_{B}\left( {{\omega_{1};\varphi},\rho} \right)}} = \begin{Bmatrix}{{P_{0}\left( {{\omega_{1};\varphi},\rho} \right)},{P_{1}\left( {{\omega_{1};\varphi},\rho} \right)},\ldots \mspace{14mu},} \\{{P_{i}\left( {{\omega_{1};\varphi},\rho} \right)},\ldots \mspace{14mu},{P_{N - 1}\left( {{\omega_{1};\varphi},\rho} \right)}}\end{Bmatrix}},}\end{matrix}$$\mspace{20mu} {{\ldots \mspace{14mu} \vdots \mspace{14mu} \ldots \mspace{14mu} \ldots \mspace{20mu} \ldots \mspace{20mu} \ldots \mspace{14mu} \ldots \mspace{14mu} \ldots \mspace{20mu} \ldots \mspace{14mu} \ldots}\mspace{14mu},{{{{{Time}\mspace{14mu} {series}\mspace{14mu} N} - {1\left( \xi_{N - 1} \right)}}:{S_{B}\left( {{\omega_{N - 1};\varphi},\rho} \right)}} = {\begin{Bmatrix}{{P_{0}\left( {{\omega_{N - 1};\varphi},\rho} \right)},{P_{1}\left( {{\omega_{N - 1};\varphi},\rho} \right)},\ldots \mspace{14mu},} \\{{P_{i}\left( {{\omega_{N - 1};\varphi},\rho} \right)},\ldots \mspace{14mu},{P_{N - 1}\left( {{\omega_{1};\varphi},\rho} \right)}}\end{Bmatrix}.}}}$

Symbol ξ_(f) represents the Bispectrum-Time-Series at frequency f. Theinventors have found that it is possible to choose particular values for‘f’ such that the Bispectrum-Time-Series, ξ_(f) carries sufficientinformation to characterize macro-sleep-stages. In Section 3, theimplementation details to estimate MSA will be explained with derivedresults.

3. Results and Discussion

Implementation details of a method according to a preferred embodimentof the invention will now be described. The results of the method willthen be compared to a set of clinical data with a view towards exploringthe method's performance.

FIG. 2 shows a block diagram according to an embodiment of the presentinvention. The following discussion will describe the method whichentails processing EEG signals, or EEG signal data, to produceindications as to which MSA states segments of the EEG signalscorrespond to and in one embodiment a sleepiness index. The varioussteps of the method are programmed as tangible instructions in acomputer readable media for execution by one or more processors of thecomputer. The computer may include a suitable analog to digitalconverter to receive EEG signals from electrodes that are in contactwith a patient. Alternatively, the computer may process previouslylogged EEG signals stored on a magnetic, electronic memory or opticalmedium for example.

3.1 Implementation Details A). Pre-Filtering of EEG Segments:

EEG is a low-frequency signal and the frequency band of present interestis contained within 1-45 Hz. Thus x^(i)(k) is filtered using a 5^(th)order, zero-phase digital Butterworth bandpass filter f(k) with lowercut-off frequency f_(i)=1 Hz and higher cut-off frequency f_(h)=45 Hz toremove out-of-band noise, including the ubiquitous power lineinterference at 50 Hz. All results shown herein have been derived withfiltered EEG data. Let the filtered segments x^(i)(k) be denoted byy^(i)(k).

B). Estimation of the HOS:

The bispectrum can be estimated via estimating the 3^(rd) order cumulant(see (2)) and then taking a 2D-Fourier transform (see (3)), this method,known as the indirect method of estimating the bispectrum, is used inthe preferred embodiment of the invention discussed herein. Theprocedure to form the estimate C^(yi)(τ1,τ2) of the cumulantC^(yi)(τ1,τ2) of y^(i)(k) is outlined in steps (S1)-(S4) below.

(S1) Segment y^(i)(k) into J records of length L samples each. Subtractthe mean of each record to form the zero-mean sub-segments y^(ij)(k).Estimate the 3^(rd) order cumulants C ^(yij)(τ₁,τ₂) of each sub-segmenty^(ij)(k) using (6) as defined in [30].

$\begin{matrix}{{{c^{yij}\left( {\tau_{1},\tau_{2}} \right)} = {\frac{1}{L}{\sum\limits_{k = 0}^{L - 1}{{y^{ij}(k)}{y^{ij}\left( {k + \tau_{1}} \right)}{y^{ij}\left( {k + \tau_{2}} \right)}}}}},{{\tau_{1}} \leq Q},{{\tau_{2}} \leq Q}} & (6)\end{matrix}$

where Q is the length of third-order correlation lags considered in thecomputation.

(S2) Average the cumulants estimates C ^(yij)(τ₁, τ₂) over all subsegments j=0, 1, . . . J−1 to obtain the overall cumulant estimate C^(yi)(τ₁,τ₂).

$\begin{matrix}{{C^{yi}\left( {\tau_{1},\tau_{2}} \right)} = {\frac{1}{J}{\sum\limits_{j = 0}^{j - 1}{{\overset{\_}{C}}^{yij}\left( {\tau_{1},\tau_{2}} \right)}}}} & (7)\end{matrix}$

(S3) Apply a bispectrum window function to the overall cumulant estimateC ^(yi)(τ₁,τ₂) to obtain the windowed cumulant function C_(w)^(yi)(τ₁,τ₂). We used the minimum bispectrum-bias supremum windowdescribed in [29] for the purpose.

(S4) The bispectrum B^(yi)(ω₁,ω₂) of the segment y^(i)(k) was estimatedas the 2-D Fourier transform of the cumulant estimate C _(w)^(yi)(τ₁,τ₂).

B ^(yi)(ω₁,ω₂)=Σ_(T) _(1=−c) ^(T) ^(1=+c) Σ_(T) _(2=−c) ^(T) ^(2=+c) C^(yi)(τ₁,τ₂)e ^(−j(τ) ¹ ^(e) ¹ ^(+τ) ² ^(e) ² ⁾  (8)

It should be noted however that the bispectrum slice may also beestimated directly in the frequency domain as

B ^(yi)(ω₁,ω₂)=γ·Y ^(i)(ω₁)·Y ^(i)(ω₂)·Y ¹*(ω₁+ω₂)  (8A)

where Y^(i)(ω₁) is the 1-Dimensional Fourier transform (1D FFT) of themeasured, filtered EEG signal and y is the skewness of y^(i).

FIG. 3 shows typical mesh and contour plots of the bispectrum magnitudeAbs(B^(yi)(ω₁,ω₂)) during different macro sleep states, S^(W), S^(N) andS^(R). Abs (•) stands for the computation of the modulii of each andevery entry. The bispectra shown in FIG. 3 were computed from short EEGsegment of length 10 s, randomly taken from the EEG data of subject ID 5(Table 3). The salient features seen in FIG. 3 were consistentlyobserved across the subjects in Databases A, B and C.

According to mesh plots in the FIG. 3, the bispectrum magnitude variesduring different sleep states. It is low during the S^(W) and S^(R)states and increases considerably during the S^(N) states. A hypothesistest (two tailed; student-t statistic) showed that the mean bispectrummagnitude during S^(N) sleep state was different from mean bispectrummagnitude during S^(W) or S^(R) states at the level of significanceσ=0.001.

The contour plots of the bispectrum magnitude in the FIG. 3 shows thelocation of the peaks at different sleep states. The comparison ofcontour plot for S^(W) or S^(R) with that for S^(N) state, shows that,bispectrum during S^(W) state have multiple peaks at differentfrequencies (ω≈1-20 Hz) whereas peak in bispectrum magnitude during NREMstate is just concentrated in narrow frequency band (ω<5 Hz). Bispectrumduring S^(R) shows multiple peaks in low frequency region (ω<10 Hz),similar to S^(W) state however, the bispectrum peak at high frequency(ω>15 Hz) completely diminishes during REM states. These features in theEEG bispectrum were consistently seen over the whole night sleep EEGdata.

C). Estimation of the Slice-Bispectogram:

Further implementation details of the method described in Section 2.22will now be discussed. Without a loss of generality (see [30]), set φ=1and set ρ=0 so that the slice of the bispectrum considered is inclinedto the ω₁-axis by 45 degrees and passes through the origin (i.e. theline described by ω₁=ω₂ in the (ω₁,ω₂)-plane) as symbolized byS_(B)(2πf;1,0).

FIG. 4( a) shows the slice bispectogram magnitude (Abs(S_(B)(2πf;1,0)).FIG. 4( b) shows corresponding sleep states scored manually by a sleeptechnician. Y-axis of FIG. 4( a) is the frequency axis, f=1-40 Hz.X-axis of FIG. 4 is the epoch number. In FIG. 4 we display the firstcontiguous 300 epochs out of the total recorded sleep epochs (N=810) forthat particular subject, for the sake of display clarity.

FIG. 4( a) clearly illustrates the variation in the bispectogrammagnitude in the different frequency bands with the change in sleepstates, consistently over the night. During the S^(W) statesbispectogram shows comparably high magnitude in the higher frequencies.With the appearance of S^(N) states magnitude in the higher frequencies(f>10) decreased whereas that of the lower frequencies (f<10 Hz)increased. Again as the sleep state changed to S^(R) states themagnitude in all the frequencies decreased.

D). Estimation of the Bispectrum-Time-Series:

From the Slice-Bispectogram (S_(B)) the, Bispectrum-Time-Series (BTS,ξ_(f)) was estimated at two frequencies S_(B)(2πf₀;1,0) andS_(B)(2πf₁;1,0). For the results reported herein, the inventors set f₀=2Hz and f₁=20 Hz for their ability to discriminate REM/NREM/WAKE states.Note that the entries of BTS are complex valued. The followingdefinitions are used from hereon: ξ₂=Abs(S_(B)(2πf₀;1,0)), andξ₂₀=Abs(S_(B)(2πf₁;1,0)). Once again Abs(•) stands for the computationof the modulii of each and every entry in BTS. The inventors have foundthat in a preferred embodiment, ξ₂ and ξ₂₀ can be used efficiently andaccurately to identify different macro-sleep states (in MSA) and also todefine a sleepiness index via the time-density of micro-sleep events. Tomake the time-series a dimensionless quantity, normalisation is appliedusing (8B) before using them for classification.

$\begin{matrix}{{{\xi_{2}(i)} = {\frac{\xi_{2}(i)}{{sum}\left( \xi_{2} \right)} \times 100}},{{\xi_{20}(i)} = {\frac{\xi_{20}(i)}{{sum}\left( \xi_{20} \right)} \times 100}}} & \left( {8B} \right)\end{matrix}$

In the case of ξ₂, pre-processing (smoothing, de-trending andequalization) is applied before using the time series in classificationwork in order to improve the performance; ξ₂₀, however did not requiresuch processing. To reveal the slow changes and remove the outliers fromξ₂, ‘Loess Smoothening Method [31, 32] is used, which is based on localregression using weighted linear least squares and a 2^(nd) degreepolynomial model. De-trending with a least-squares-fit straight line wasused to remove the trend in ξ₂. Histogram equalization is often used inimage processing to increase the contrast of the image. In the presentlydescribed embodiment, histogram equalization is applied to ξ₂ after thesteps of smoothening and de-trending to obtain the time series ξ′₂. FIG.5 shows the effect the pre-processing on ξ₂.

FIG. 6( a) and FIG. 6( b) show the ξ_(f), at frequencies 2 Hz and 20 Hzrespectively. FIG. 6( c) shows technician scored macro sleep states. EEGdata x^(i)(k) for this figure is taken from subject ID 6 of Database A,Table 3. FIGS. 6( a), 6(b) and 6(c) illustrate explicitly thecharacteristics of Slice-Bispectogram. The magnitude of 2 Hz componentvaries in a cyclical fashion, synchronously with the macro sleep states.Magnitude is high during the S^(N) sleep states and it decreased duringS^(R) and S^(W) states. The magnitude of the 20 Hz componentconsistently remained low during the sleep; however it increasedconsiderably with the episodes of S^(W) states during the night. Thus,ξ₂₀ is a preferred platform for separating wake from other sleep states.

The steps for estimating the MSA from the two bispectrum time series ξ₂₀and ξ′₂ according to the preferred embodiment, will now be described.The following method is also illustrated in the flowchart of FIG. 7.

E). MSA Scoring Algorithm Based on Bispectrum Time Series ξ′₂ and ξ₂₀

Bispectrum time series, ξ₂₀ and ξ′₂ were used to classify each datasegment x^(i)(k) (see (1)) into the classes WAKE (S^(W))/REM(S^(R))/NREM (S^(N)) sleep. The length of data segments (M) were set tothe standard ‘epoch length’ of M=30 s as used in routine sleep scoring.

The classification of the segment x^(i)(k) into the two categoriesWAKE/SLEEP can be done quite easily based on ξ₂₀ (see FIG. 6( b)).However, it cannot be used to differentiate between SLEEP states NREMand REM. The series ξ′₂ can be used easily to identify NREM segments,but cannot be used to separate WAKE from REM stages. According to thepreferred embodiment of the invention, both ξ₂₀ and ξ′₂ are usedtogether in a sequential decision process to classify x^(i)(k) into thethree categories S^(W)/S^(R)/S^(N) sleep.

The identification of sleep states was done based on direct thresholdoperations on the ξ′₂ and ξ₂₀ as described in steps (T1)-(T3) below:

-   (T1) WAKE/SLEEP Classification: For classifying each i=1, 2, 3 . . .    N, segments of EEG data x^(i)(k) into S^(W) and SLEEP (S^(R)/S^(N))    states we used ξ₂₀. We set a threshold ε₂₀ computed from ξ₂₀. The    condition ξ₂₀≧ε₂₀ was tested for all the N segments. If condition    ξ₂₀≧ε₂₀ is true in a segment then that segment was classified as    S^(W) else as SLEEP.-   (T2) NREM/REM Classification: After the step (T1), the EEG data    segments are separated into two states WAKE and SLEEP. To further    classify sleep segments into NREM and REM sleep, used ξ′₂ is used.    The threshold ε₂ is previously computed from ξ₂. The condition ξ₂≧ε₂    is tested in each segment previously classified as Sleep in step    (T1). If the condition is satisfied then that segment is classified    into NREM sleep else into REM sleep. The method for calculating    proper thresholds (ε₂ and ε₂₀) is described in Appendix F.-   (T3) Estimation of Sleep Parameters: From the estimated sleep states    of each patient, we computed TST, SE, RSL and percentage of S^(W),    S^(R) and S^(N) sleep, using the standard definition given in    Appendix B.

3.2 Performance Evaluation of the MSA Scoring Method on Clinical Data

For the purposes of this specification, macro sleep states estimatedusing the preferred embodiment of the present invention are termed theHOS based Estimated Sleep States (HESS), and the reference statesestimated by the expert human scorer is called the Technician ScoredSleep States (TSSS). The TSSS will be considered the ground truth,against which the performance of the HESS will be compared.

The performance of the HESS was evaluated on a clinical database ofEEG/PSG data (see Table 3.1, Database-A), and compared with that of theTSSS estimate. In order to test the stability and reliability of thealgorithm, subjects with varying degrees of OSAHS severity wereincluded. The test subjects population (23 subjects) had the mean RDI of27.23±26.24, ranging from as low as 0.6 and as high as 94.4. The meanarousal index (ArI) for the subject population was 29.05±19.23.

To compute the HESS, EEG data from only one channel is required. InDatabase A, EEG data were recorded with the electrode positions C4, C3,A1 and A2 based on the standard international 10-20 system of electrodeplacement. The results presented in this section are based on the EEGdata from the electrode position ‘C4’ with reference to ‘A2’.

To compute the TSSS, an expert human Sleep Scientist manually scored PSGdata based on R&K criteria [7] and clinical practice parametersformulated by the AASM. As known from previous studies, the intra-scorervariability for the Sleep Scientist was 84%. According to the TSSSscoring, of the total of 20,714 epochs from the 23 subjects, 3508 werein S^(W), 14098 were in S^(N) and 3108 were in S^(R).

The performance of the HESS in scoring macro sleep states was evaluatedin several different ways. Using TSSS as the ground truth, the followingperformance measures were computed:

-   -   Sensitivity, Accuracy and the Positive Predictive Value (PPV).    -   The agreement between TSSS and HESS using Cohen's Kappa        statistic [31] (please see Appendix D on how to interpret Kappa        values).    -   Pearson's correlation coefficient and Altman-Bland plots [34]        were used for the descriptive analysis of TST, SE, RSL and        percentage of S^(W), S^(N), S^(R) sleep computed from HESS.    -   Hypothesis testing to establish the statistical significance of        the results at the level of significance σ=0.001.

FIG. 9 (a)-(b) and FIG. 10 (a)-(b) shows the PSG scored sleep states andautomated estimated sleep states after Step (T2) of section 3.1 (E), fortwo subjects (Subject ID=6 and 19). According to these figures there ishigh resemblance between the technician scored sleep states andautomated sleep states. The agreement between TSSS and HESS inclassifying N epochs (N=810 for subject 6 and N=1018 for subject 19)into S^(W), S^(N) and S^(R) was 78% (kappa 0.61) and 80% (kappa=0.69)respectively for FIG. 9 (b) and FIG. 10 (b).

In FIG. 9( b) and FIG. 10( b) a few brief episodes of REM are seen,which are not REM sleep episodes according to TSSS. From the literatureit is known that the duration of a typical REM episode is between 10-20minutes in first sleep cycle which increases in the latter half of thenight. Hence, in order to decrease False positive prediction of the REMepisodes and to increase the accuracy of HESS a REM-continuity rule wasused. According to this rule those periods of REM sleep were targetedwhich were for less than D mins. These periods of S^(R) sleep werereclassified into either S^(N) state or S^(W) states. For thereclassification a new threshold ε′₂₀ was defined from the ξ₂₀ timeseries. Appendix F shows the method to compute D and ε′₂₀. In thepreferred embodiment of the present invention D=8 min and ε′₂₀=min(ξ₂₀).If condition ξ₂₀=ε′₂₀ was satisfied then that epoch was classified asS^(W) else as S^(N).

FIG. 9( c) and FIG. 10( c) shows the HESS results after applying REMcombining rule. After the rule agreement between TSSS and HESS increasedto 81% (kappa=0.66, σ=0.001) and 86% (kappa=0.71, σ=0.001) respectivelyfor FIG. 9 and FIG. 10. REM combining rule was used in all the furtherclassification.

Table 5 shows the contingency table, comparing the scoring performanceof the HESS with that of TSSS. Table 6 gives the sensitivity, accuracyand PPV statistics for HESS computed from the Table 5. The overallagreement between TSSS and HESS in separating the 20,714 epochs into thetwo states Sleep (S^(N) and S^(R) pooled together) and Wake is 83.78%(kappa=0.48, σ=0.001, z=61). Also, there was a high agreement betweenTSSS and HESS in classifying S^(W), S^(N) and S^(R) states, of 75.17%(kappa=0.54, σ=0.001, z=94). Table 7 lists the agreement and kappavalues between TSSS and HESS for all the 23 subjects. The mean agreementbetween TSSS and HESS for 23 subjects was 77.6%±7.2% and mean kappavalue was 0.58±0.08.

In the PSG test several sleep parameters such as, Total sleep time(TST), Sleep efficiency (SE), REM sleep latency (RSL) and percentage ofS^(W), S^(N), S^(R) sleep are directly computed from the scored MSA.Accurate estimation of these parameters substantially depends on theprecise classification of macro sleep states. These parameters werecomputed for each patient from the HESS and compared with those computedusing TSSS.

FIG. 11 consists of scatter plots, showing the relationship between theparameters TST, SE, RSL and percentage sleep computed using TSSS andHESS. The correlation for TST, SE and RSL was 0.84(σ=0.001),0.62(σ=0.001) and 0.79(σ=0.001) respectively.

FIG. 12 show the Altman-Bland plot for TST, SE, RSL and percentagesleep. These plots show that there is a good agreement between TSSS andHESS in estimating MSA parameters with very small insignificant bias.The bias for TST was −16 minutes (σ=0.32) and for SE was only −3.7%(σ=0.24). The bias for RSL and percentage of S^(W), S^(N), S^(R) sleepwere also low and insignificant, 14 min (σ=0.26), 1.3% (σ=0.34), −4%(σ=0.13) and 2.8% (σ=0.10) respectively.

3.3 Performance of HESS Algorithm with Other Electrode Position

The performance of the previously described HESS method according to apreferred embodiment of the invention will now be discussed, when EEGfrom electrode locations other than the traditional (R&K-recommended,1968) C3, C4, A1 and A2 positions are used. For this data from thesubjects in database B, table 3.2 was used. The International 10/20electrode locations (see FIG. 13), were investigated with a view towardsidentifying the best electrode locations suitable for single-channelEEG-based sleep scoring.

Following the customary practice in sleep EEG, site Fpz was used for theground electrode. The reference electrode for measurement sites locatedon the left hemisphere of the scalp was A1; similarly, measurements onthe right hemisphere used A2. This choice of the ground and referenceelectrodes comply with the R&K recommended measurements on C3 and C4.This makes a direct comparison between the performances of traditionaland the HESS-based techniques possible.

FIG. 14 shows the estimated sleep states using HESS algorithm using EEGdata from the electrode locations Fp2, F3, Fz and P3 over the scalp.Table 8 summarizes the statistical results for all electrodes that wereused. A statistically significant (σ=0.0001) agreement between TSSS andHESS for all the electrode positions was found.

3.4 Sleepiness Index

The time series ξ₂₀ proved highly successful in classifying EEG epochsinto the two groups Sleep and Wake. Wake states correspond to highvalues of ξ₂₀; sleep states are associated with a consistently lowmagnitude (s₀) approaching zero for all practical purposes. It was notedthat the magnitude of ξ₂₀ gradually moves from a high value towards s₀as a person is falling asleep. Before finally settling down to s₀corresponding to the state Sleep, the magnitude of ξ₂₀ briefly touchess₀ several times (see FIG. 15 (a)). Such events are associated with EEGsthat are characteristic of Sleep, and the inventors define them asmicro-sleep events.

According to a preferred embodiment of the present invention, thestability of the ξ₂₀ time series to identify micro-sleep events and thenuse them to form a measure of sleepiness, termed the Sleepiness Index(SI). We define the SI as the fraction of the time the magnitude of ξ₂₀maintained its value corresponding to sleep, i.e., s₀, computed over atime frame of 10 seconds. Thus, SI can vary from 0 (no episodes ofmicro-sleep during the current 10 s period) to 1 (micro-sleep/sleepevents completely covers the current 10 s period).

This definition allows the SI index to be computed real time, making ita useful tool to monitor the sleepiness of an individual. Sleep Onsetcan be defined as the instant at which SI reaches 1, and the SleepLatency can be defined as the time duration from the ‘lights out’ to theSleep Onset. The SI can be applied to automate the scoring of MSLT andthe MWT tests, which are two important diagnostic tools used in generalsleep medicine. In addition to the use in sleep medicine, the SI-indexhas the potential to be used in situations such as the sleepinessmonitoring of long-range truck drivers; to facilitate this use, SI hasbeen defined in such a way that it can be computed in real-time.

In FIG. 15( a) we show the time series ξ₂₀ where a person is fallingsleep. The red bars under the series ξ₂₀ curve indicate micro-sleepevents. The TSSS sleep states are shown in FIG. 15( b). Note that thesleep technologist has not attempted to identify states of micro-sleep.FIG. 15( a) and FIG. 15( b) graphically illustrate the closecorrespondence of the ξ₂₀ and the sleep/wake states of an individual.Moreover, it is seen that the gradual development of sleep over time iscaptured well by the gradual change in ξ₂₀.

To test the capability of the SI to estimate the sleep onset and thesleep latency (SL), we computed SI for the subjects in Database C. Inall the computations, the segment length was M=30 s, and the segmentoverlap was set to 29 s. Thus, the sleep state was assessed everysecond, based on the data of duration 30 s. The SI index was computedusing the sleep state information covering a period of 10 s. FIGS. 17and 18 shows the SI and TSSS for the subject ID 28. Data in FIG. 17 andFIG. 18 is from the 2^(nd) and 3^(rd) nap of the MSLT test (see AppendixE for details of the MSLT test). Table 9 gives the SL as computed fromthe SI and TSSS for all the 4 naps in the MSLT for all the subjects indatabase C.

According to Table 9, the SL computed via the SI closely matches theSleep Technologist identified Sleep Latency. The SI based approach,however, has been fully automated and provides consistent results. Itdoes not depend on multiple physiological signals; only one channel ofEEG is sufficient for the purpose. On the contrary, Sleep Technologistrequires multiple signals and depends on subjective methods to estimatethe SL.

Finally, it is of great interest to explore how the micro-sleep eventsdefined via ξ₂₀ correspond with micro-sleep defined by the AASM. As anillustration, in FIG. 16 we show the EEG (C4-A1, C3-A2), EOG (left andright eye) and EMG data from the epochs 35, 36, 37, and 38 as marked onFIG. 15( a). Epochs 35 and 36 of FIG. 16 clearly follow the micro-sleepcharacteristics as defined by AASM, whereas epochs 37 and 38 do not. Themicro-sleep defined by AASM agrees with the micro-sleep events definedvia ξ₂₀ according to the preferred embodiment of the present invention.

The SI index that has been described can be used to identify the levelof sleepiness of an individual, on a real-time basis. This is expectedto contribute significantly to the monitoring of sleepiness of truckdrivers etc. with a view towards timely intervention to prevent eventsof micro-sleep/sleep and accidents.

4.1 A Dedicated Sleep Architecture Estimator

Referring now to FIG. 19 there is depicted a block diagram of a SleepArchitecture Estimator 1 according to an embodiment of the presentinvention. The following embodiment makes use of circuit blocks that arearranged to analyse bispectra, however as previously discussed, otherhigher order spectra might also be used such as the trispectrum. It willbe realised that the blocks that are depicted are provided as separateinterconnected circuit modules, as shown. Alternatively in anotherembodiment they may be implemented as virtual modules by a machine suchas a suitably programmed high speed computer, preferably including oneor more dedicated digital signal processing integrated circuits. In thelatter alternative an aspect of the invention encompasses a programstorage device, for example an optical or magnetic disk or memorycircuit. The program storage device is readable by the machine andtangibly embodies a program of instructions executable by the machine tocause the machine to perform a method according to the presentinvention, for example preferably the method described above in sections2. and 3. The computer preferably includes a suitable analog to digitalconverter with ports to receive EEG electrodes for monitoring a subject.The computer also includes a graphical display for displayingclassifications of sleep that are determined during execution of theprogram for viewing by a human operator. A keyboard and mouse are alsoincluded in order that the operator can vary parameters of the program,such as thresholds for example.

The Sleep Architecture Estimator 1 includes ports for connection of EEGelectrodes 2. The ports are connected to an analogue signal conditioningmodule 2, which contains circuitry receiving and conditioning the EEGsignals. This circuitry typically includes AC coupling to high inputimpedance differential amplifiers, noise filtering and limiting. Theconditioned analogue signal is passed to an analogue to digitalconverter module 6 which includes an antialising filter. The digitalsignal from ADC 6 is passed to a bandpass digital filter 8 to removeout-of-band noise, including mains power distribution frequency hum.

Modules 2 to 8, as described above may collectively be referred to as anEEG digital signal assembly 3, for converting analogue EEG signals intodigital EEG signals.

A Segmentation Module 10 is coupled to the output of the bandpassdigital filter 8 in order to receive the filtered digital signal.Segmentation Module 10 is arranged to segment the filtered digitalsignal from the bandpass digital filter 8 into a number of records, eachcomprising a plurality of samples. The Segmentation Module 10 is furtherarranged to subtract the mean of each record to form a digital signalrepresenting zero-mean sub-segments. A 3^(rd) Order Cumulant Calculator12 is coupled to the output of segmentation module 10. The CumulantCalculator 12 is arranged to produce a signal representing an estimateof the 3^(rd) order cumulants of each sub-segment. An Averaging Module14 receives the 3^(rd) order cumulant signal and processes it to producean overall cumulant estimate signal.

A Bispectrum Window Processor 16 receives the overall cumulant estimatesignal and is arranged to produce a windowed cumulant signal inaccordance with a minimum bispectrum-bias supremum window.

The output of the Bispectrum Window Processor 16 is coupled to a 2-DFourier Transform Module 18, which is arranged to process the windowedcumulant signal in order to produce a bispectrum signal that representsan estimate of the bispectrum of the corresponding segment.

Modules 10 to 18, as described above, may collectively be referred to asa bispectrum assembly 13 to convert the digital EEG signals frombandpass digital filter 8 into signals representing bispectrum values.

In an alternative embodiment the bispectrum assembly is arranged todirectly determine the bispectrum estimate according to equation (8A) aspreviously described. According to the direct calculation embodiment ofthe invention the Cumulant Calculator 12 is not required. Direct methodestimation is computationally much less expensive, but has somewhathigher estimation variance (leading to poorer performance).

A Slice Bispectogram Estimator 20 receives the output from the FourierTransform Module 18 and is arranged process the bi-spectrum signal tocalculate values for a slice of the bispectrum. As previously described,the particular slice of the bispectrum that is favoured is the linedescribed by ω₁=ω₂ in the (ω₁,ω₂)-plane. The Slice BispectogramEstimator calculates the values for the slice and generates arepresentative digital signal that is output to the Bispectogram TimeSeries Estimator 22.

The Bispectrum Time Series Estimator 22 is arranged to process theoutput from the Slice Bispectogram Estimator in order to produce twoBispectrum Time Series data signals at first and second frequencies f₀and f₁. These two frequencies are preferably set at 2 Hz and 20 Hzrespectively although they may be user adjusted within limits.

Modules 20 and 22 may be collectively referred to as a bispectrum timeseries assembly 21 for generating bispectrum time series at the firstand second frequencies.

A pre-processing module 24 receives the output from the BispectogramEstimator and is arranged to operate on the 2 Hz time series data signalto apply smoothing, de-trending and histogram equalization.

The pre-processed 2 Hz time series data signal, and the 20 Hz timeseries data signal, are passed to an MSA estimator module 28. The MSAestimator is arranged to processes the time series data signals andincludes comparators to compare them to predetermined threshold valuesas described in steps T1, T2 and T3 of section 3.1 E above. On the basisof those comparisons the MSA Estimator 28 produces a sleep state signalthat indicates any one of Waking, Non-REM and REM sleep states. Thesleep state signal is passed to a User Interface 32, as will bediscussed further below. In another embodiment of the invention furthermodules may be incorporated to provided a method of diagnosingobstructive sleep based on the determined macro sleep states.

A Sleepiness index Calculator 26 processes the 20 Hz time series datasignal from the Bispectogram Time Series Estimator 22. The SleepinessIndex Calculator 26 is arranged to determine the fraction of the timethat the magnitude of the 20 Hz time, in each segment, that the seriesdata signal maintains a value approaching zero (i.e. at or less than(s_(o)) as discussed in section 3.4 above).

Modules 24 to 28 may be collectively referred to as a macro-sleeparchitecture assembly 25 for producing classification signals indicatingclassification of segments of the EEG signals into macro-sleep statessuch as Sleep, Wake and NREM sleep and REM sleep.

A User Interface 32 is provided which communicates with the MSAEstimator module 28, the Sleepiness Index Calculator 26, the BandpassDigital Filter 8 and the Bispectogram Time Series Estimator 22. The UserInterface 32 includes a visual display 36 to display information to auser such as the EEG waveform, sleep state, total sleep time, current f₀and f₁ values and current sleepiness index value. Operator buttons 38are disposed about the screen to allow an operator to enter controlparameters via an interactive menu system which is displayed upon thescreen in use.

FIG. 20 shows the Sleep Architecture Estimator in use connected, viaelectrodes 40 to a patient 42 with display 36 showing the patient's EEGsignal and particular information in regard to the patient's sleepstate.

The Sleepiness Index may be used for in-facility MSLT and MWT tests,i.e. automated, objective estimation of sleep onset, sleep latenciesetc, using just one EEG channel. Furthermore, the described apparatusmay be used to automatically score macro sleep states in overnight PSGtests.

4.2 A Driver Sleepiness Monitoring System

Referring now to FIG. 21, there is depicted a block diagram of a DriverSleepiness Monitoring System 44, according to an embodiment of thepresent invention in use.

The sleepiness monitoring system includes a Sleepiness Index Alert Unit46 which incorporates modules 2 to 22 and 26 of the Sleep ArchitectureEstimator 1 of FIG. 19. The Sleepiness Index Alert Unit 46 receives EEGsignals from driver 48 via a headband mounted set of electrodes 50 andin responses produces an ongoing sleepiness index. Although a cableconnection is shown to the set of electrodes 50, a wireless connectionmay be used instead.

In the event of the sleepiness index rising above a predeterminedthreshold for longer than a minimum time frame, or more than a minimumnumber of times in a predetermined time period, then the SleepinessIndex Alert Unit 46 is arranged to activate alarm 50, which requiresmanual switch-off by the driver within a specified time period, in orderto increase the wakefulness of driver 48.

In the event of the sleepiness index falling below a predeterminedthreshold for longer than a minimum time frame, or more than a minimumnumber of times in a predetermined time period, then the SleepinessIndex Alert Unit 46 is arranged to activate the driver interventionmodule (eg. alarm) 50 in order to increase the wakefulness of driver 48.

The Sleepiness Index Alert Unit 46 also communicates with a VehicleControl Interface 54 that in turn is arranged to control the vehicle'sengine via the engine management system 56.

A data logger 52 is provided, that is coupled to the Sleepiness IndexAlert Unit 46 and which is arranged to record EEG and sleepiness indexdata of the driver 48. A wireless communications module 58, capable ofcommunicating with a home base via a cellular phone network, is coupledto the Sleepiness Index Alert Unit. Consequently the Sleepiness IndexAlert Unit 46 is able to send and receive data relating to the driver'sstate of wakefulness to the home base. This information may be used toorganise a replacement driver for example.

The Driver Sleepiness Monitoring System 44 may also be equipped with abiofeedback assembly containing an audio stimulus source, e.g. MP3recordings or radio receiver, to provide stimulus such as music, sportscommentaries, etc. to the driver in order to lower the driver'ssleepiness index. A continual display of the sleepiness index may alsobe displayed to the driver including prompts, for example requests forthe Driver to stop for a cup of coffee, upon the sleepiness indexclimbing above a predetermined threshold.

5. Conclusion

A method and apparatus for estimating Macro Sleep Architecture (MSA) andSleepiness

Index (SI) as the indicator of sleepiness, using single channel of EEGdata, has been described.

The inventors' intensive statistical analysis of the 20,714 epochs from23 subjects showed a significant agreement between the proposedHigher-Order-Spectra Based technique (HESS) and Technician Scored SleepStates (TSSS). On an average there was an agreement of 77.63% (±9.9%)between TSSS and HESS in classifying sleep into three macro sleepstates, State Wake (S^(W)), State REM (S^(R)) and State NREM (S^(N)).This level of agreement is considered an excellent result in clinicalsleep scoring, observing that it is on a par with intra-scorer agreement[12] of expert human technologists. Note that the agreement reported onthe HESS technique was computed on subjects spanning a large range ofRDI, whereas the intra-scorer agreement of humans deteriorates furtherin the presence of OSAHS cases [13]. In addition, the HESS requires onlyone channel of EEG, whereas the TSSS uses multiple modes of signals.

The MSA parameters such as NREM/REM stages, Total Sleep Time, Sleepefficiency and REM sleep latency were computed by HESS withsignificantly high accuracy and correlation, using fully automatedalgorithms. These are the most important EEG-based parameters resultingfrom a PSG test. Our ability to compute the parameters reliably,objectively and using a single channel of EEG should make a dramaticimpact on the diagnosis and treatment of OSAHS as well a range of othersleep disease such as insomnia.

MSLT and MWT are the main measures of sleepiness used in the clinicalenvironment. They are, however, complicated tests requiring access tosleep laboratories and the services of experienced sleep technologists.Even then, the computation of parameters such as the SL and Sleep Onsetare fraught with subjective elements. The Sleepiness Index provides anelegant solution to these problems. It can be automatically computedfrom a single channel of EEG data at real-time.

SI index is reliable and it is expected that it can be also used as atool to monitor the sleepiness of humans in hazardous environments suchas driving or operating mining equipment.

APPENDIX A

In Appendix A we provide the standard definitions of Apnea, Hypopnea andArousals as formulated by professional sleep disorders organizationssuch as the AASM [9] and ASDA [33]. These definitions are routinely usedby sleep physicians around the world to diagnose and treat OSAHS.

Definition of Apnea (AASM)

-   -   Cessation of airflow ≧10 s (with oxygen desaturation undefined)        OR    -   Cessation of airflow <10 s (but at least one respiratory cycle)        with oxygen desaturation ≧3%.    -   These events can occur with or without arousal

Definition of Hypopnea (AASM): an event to be defined as hypopnea itshould fulfil criterion (a) or (b), plus (c) of the following:

-   -   (a) A clear decrease (≧50%) in amplitude from base line of a        valid measure of breathing during sleep. Baseline is defined as        the mean amplitude of stable breathing or the mean of three        largest breaths in the two minutes preceding onset of the event.    -   (b) A clear amplitude reduction but does not reach criteria (a)        but is associated with oxygen de-saturation >3% or an arousal.    -   (c) Event lasts ≧10 seconds.

Definition of Arousals (ASDA) [33]: American Sleep Disorder Associationdefines EEG arousals (EEGA) as abrupt shift in EEG frequency, which mayinclude theta, alpha activity and/or frequencies greater than 16 Hz (butnot sleep spindles) subjected to following scoring rules:

-   -   the subject must be asleep for a minimum period of 10 s before        declaring an Arousal event,    -   EEG frequency shift must be sustained for a 3 s duration or        more, and,    -   EEG arousal from REM sleep requires presence of simultaneous        increase in the sub mental EMG amplitude.

APPENDIX B [10]

Respiratory Disturbance Index (RDI) is defined as the average number ofapnea/hypopnea events per hour of sleep.

Arousal Index (ArI) is defined as the average number of arousal eventsper hour of sleep.

Total time in Bed (TTB) is the time spent on the bed from the ‘lightsoff’ when the recording starts in the night to the ‘lights on’ in themorning when the recoding ends.

Total Sleep Time (TST) is defined as the actual sleep time (total timespend in REM and NREM sleep).

Sleep Efficiency (SE) is defined as the percentage ratio 100× (TST/TTB)%.

REM sleep Latency (RSL) is defined as the time between the sleep on-setand the first occurrence of REM sleep.

APPENDIX C [6]

Rectschaffen and Kales (R&K) in 1968 [11] laid the rules for manualscoring of sleep stages. These rules are very broad and complex, in herewe are just giving a short summary of these rules. R&K rules requireEEG, EMG and EOG channels for implementation.

Macro Sleep State Wake (S^(W)): When a person is awake and active, EEGshows high beta activity (frequency >16 Hz) with low voltage. As theperson starts relaxing (with eyes closed) and gets drowsy EEG showsabundance of alpha (8-12 Hz) activity. Voluntary random slow eyemovements can be seen in EOG. EMG is tonic with relatively highvoluntary activity.

Macro Sleep State NREM (S^(N)): after the sleep onset, EEG shows lowvoltage and mixed frequency activity. Theta (4-7 Hz) activity increases.The NREM state is further subdivided into four stages; in Stage 1features such as sharp vertex waves appear; EOG shows slow eye movement;EMG activity decreases. In Stage 2, EEG activity is full of featuressuch as spindles and k-complexes; EOG activity disappears; EMG showsvery low tonic activity. In Stage 3 and Stage 4 EEG shows an abundanceof delta activity (high voltage (>75 microV), low-frequency (<3 Hz)waves); no EOG activity and very low tonic EMG activity.

Macro Sleep State REM (S^(R)): EEG shows low voltage mixed frequency(range of high theta and slow alpha) sawtooth wave like activity. EOGshows phasic random eye movement activity. In EMG tonic activity getssuppressed and phasic twitches appears.

APPENDIX D [6, 34, 35]

Kappa statistic is widely used in situations where the agreement betweentwo techniques should be compared. In Appendix D we provide a guidelinefor interpreting the Kappa values.

Kappa Interpretation [31] <0 Less than chance agreement 0.01-0.20 Slightagreement 0.21-0.40 Fair agreement 0.41-0.60 Moderate agreement0.61-0.80 Substantial agreement 0.81-1   Almost perfect agreement

APPENDIX E [6, 28]

Multiple Sleep Latency Test: In MSLT a series of nap opportunities (4-6)are presented to the subject undergoing test at 2 hour intervalsbeginning approximately 2 hour after morning awakening [6]. Subjects inMSLT test are instructed not to resist themselves from falling sleep.Electrophysiological signals which are recorded to detect sleep onset inMSLT are (i) 4 channel EEGs (C3, C4, O1, O2 with reference to A1 andA2). (ii) left and right eye Electro-oculograms (EOGs), and (iii)submentalis electromyogram (EMG). Each MSLT recording goes for at least20 minutes. If no sleep onset occurs within 20 minutes than napopportunity is terminated. If sleep onset occurs before 20 minutes thantest session is continued for 15 minutes after sleep onset. SleepLatency in MSLT is defined as the elapsed time from the start of thetest to the first 30-sec epoch scored as sleep. Pathological sleepinessis defined if mean SL is less than or equal to 5 minutes.

Maintenance of Wakefulness Test: The test procedures are similar toMSLT. The only difference is the objective of the test and instructionsgiven to the test subject. The subject is instructed to attempt toremain awake and objective of the test is to assess the function of theunderlying wakefulness system.

APPENDIX F

The method of computing values for the thresholds, ε₂, ε₂₀, ε′₂₀ and Dused in the REM combining rule, used in Section 3.1, (E) and section 3.2respectively will now be described.

(a) Thresholds (Δ), ε₂ and ε₂₀: Values for the thresholds (Δ) ε₂₀ and ε₂were decided after a careful search process, such that the Agreementbetween HESS classification (WAKE/NREM/REM) and TSSS was optimized.Initially ε₂ and ε₂₀ were set at high values. Both the thresholds werethen decreased consecutively in several steps and Δ chosen as thecombination of ε₂ and ε₂₀, please see (9)-(11). For each combination(300) of thresholds set the agreement between TSSS and HESS was computedin estimating, S^(W), S^(N), S^(R). Those values for ε₂ and ε₂₀ forwhich agreement was optimised were selected. FIGS. 13A and 13Bgraphically illustrate the results of this search. From the FIG. 7 itwas possible to obtain the absolute values for ε₂₀ and ε₂ at whichagreement between HESS and TSSS is optimised. However, it was observedthat by using varying threshold (threshold varies for every patientdepending on the BTS) such that ε₂₀=median(ξ₂₀)+c and ε₂=min(ξ′₂), theagreement between TSSS and HESS further increases, FIG. 13A, 13B, where‘c’ is a constant, whose value was set at 0.03 after a search process.These values of Δ were used in step (T1) and (T2) of section 3.1. Atthese values the best overall agreement between TSSS and HESS of 75.17%was achieved. The agreement was 74 and 73 when c=0.02 and 0.04respectively.

ε₂ε{1,0.67,0.44,0.3,0.20,0.13,0.09,0.058,0.04,0.026,0.017,0.01,0.007,0.005,0.003,0.0022,0.0015,0.001,0.00067,0.0004}  (9)

ε₂₀ε{10,5,2.5,1.25,0.625,0.312,0.156,0.078,0.039,0.019,0.009,0.0048,0.0024,0.0012,0.0006}  (10)

Δ={ε₂,ε₂₀}  (11)

(b) D for REM-combining Rule and Threshold ε′₂₀: To get the bestestimation of D, we varied D from 0 to 10 minutes and applied HESSalgorithm on the subjects from the database A. Again D was fixed after acareful search process, at which agreement between TSSS and HESS wasoptimised. FIG. 8 (AT the end of this document) shows the variation ofagreement with D. From the FIG. 8, D=8 min was set. Epochs excluded fromthe REM sleep state due to REM rule were re-classified into NREM sleepor WAKE state. We used BTS at f=20 Hz for the re-classification as ithas shown the excellent ability to distinguish sleep from wake (see FIG.5). We set the threshold constant at ε′₂₀=median(ξ₂₀). We checked forthe condition ξ₂₀>ε′₂₀. If condition was satisfied then epoch wasreclassified as S^(W) else as S^(N).

TABLE 1 List of Abbreviations used herein OSAHS Obstructive Sleep ApneaHypopnea Syndrome PSG Polysomnography TTB Total Time in Bed TST TotalSleep Time SE Sleep Efficiency RSL REM Sleep Latency TSSS TechnicianScored Sleep States HESS Higher order Estimated Sleep States RDIRespiratory Risturbance Index AHI Apnea Hypopnea Index ArI Arousal IndexEEG Electroencephalogram EOG Electro-occulogram EMG Electro-myogram ECGElectro-cardiogram R&K rules Rechtschaffen and Kales rules REM Rapid EyeMovement NREM Non Rapid Eye Movement HOS Higher Order Statistics BTSBispectogram-Time-Series PPV Positive Predicted Value

TABLE 2 List of Symbols used herein S^(W) Macro Sleep State “wake” S^(N)Macro Sleep State “NREM” S^(R) Macro Sleep State “REM” ω DiscreteFrequency (radians per second) r Pearson's Correlation Coefficient σSignificance level in statistical tests z Z statistics in hypothesistest t t statistic in hypothesis test δ Delta frequency range of EEG θTheta frequency range of EEG α Alpha frequency range of EEG β Betafrequency range of EEG N Total Number of EEG Epochs ξ_(f) BispectogramTime Series at frequency f

TABLE 3 Demographic details of the subjects studied. Sub. ID Age Sex BMIRDI (3.1) Database A: Subjects with PSG recordings 1 45 F 32.8 0.6 2 34F 23.5 1.5 3 62 F 28.68 3.5 4 63 F 20.6 4.1 5 36 F 34.1 4.7 6 52 M 24.54.9 7 44 M 38.8 5.6 8 44 F 49.2 6.8 9 40 F 27.6 8.5 10 44 F 38.1 13.8 1135 M 28.7 16.8 12 50 F 20.1 18.3 13 50 M 31.8 20.8 14 69 F 23.8 23.8 1561 M 28.1 33.5 16 71 F 29.7 33.6 17 62 M 30.1 37.5 18 56 F 34.4 40.1 1963 F 41.05 45.8 20 29 M 36.8 48.2 21 47 M 31.8 60.1 22 33 F 39.7 83.1 2327 M 45.5 94.4 (3.2) Database B: Subjects with PSG + Full EEG recording24 27 M 23.7 7.1 25 32 M 30.2 18.5 (3.3) Database C: Subject with MSLTrecording Sub. ID Age Sex BMI SL 26 34 M 26.6 5.5 27 57 F 19.6 15.6PSG—Polysomnography, EEG—Electroencephalogram, MSLT—Multiple SleepLatency Test.

TABLE 4 Variation in Accuracy and PPV value of HESS with change inThreshold, ε₂ and Threshold, ε₂₀ used in the section 3.1 and blockdiagram of FIG. 7. Min Min + (μ − min)/2 μ μ + (max − μ)/2 max (S^(N) +(S^(N) + (S^(N) + (S^(N) + (S^(N) + ε₂ S^(W) S^(N) S^(R) S^(R)) S^(W)S^(N) S^(R) S^(R)) S^(W) S^(N) S^(R) S^(R)) S^(W) S^(N) S^(R) S^(R))S^(W) S^(N) S^(R) S^(R)) ε₂₀ Accuracy 96 10 0 8 96 10 0 8 62 74 74 87 3674 88 87 28 74 92 87 Min PPV 18 88 — 88 18 88 — 87 50 88 52 92 59 86 4371 58 86 40 69 Accuracy 97 8 0 6 97 8 0 7 64 67 77 69 40 67 90 72 32 6594 71 {Min + PPV 17 88 — 88 17 88 — 88 48 88 45 74 53 87 39 68 51 88 3666 (μ − min)/2} Accuracy 97 7 0 6 97 7 0 6 65 61 78 64 43 61 90 66 34 5894 64 μ PPV 17 88 — 88 17 88 — 88 45 88 41 70 48 87 36 65 46 89 33 61Accuracy 98 4 0 3 98 4 — 3 72 33 61 38 55 30 79 39 48 27 86 38 {μ + PPV17 88 — 88 17 88 — 88 28 84 31 57 28 86 27 48 27 89 26 45 (max − μ)/2}Accuracy 100 — — — 100 — — — 100 — — — 100 — — — 100 — — — Max PPV 17 —— — 17 — — — 17 — — — 17 — — — 17 — — — In our classification algorithmwe used two Bispectrum time series, ζ′₂ and ζ₂₀. ε₂ was computed fromζ′₂ and ε₂₀ from ζ₂₀. To find out the best value for ε₂ and ε₂₀ wevaried both and computed accuracy and PPV of HESS. As we can see fromthe table at ε₂₀ = mean(ζ₂₀) and ε₂ = minimum(ζ′₂), HESS gives the bestresults. Min is the Threshold set at the minimum value in the timeseries, Max is the threshold set at maximum value of time series andmean is the mean value of the time series.

TABLE 5 Contingency table for TSSS vs. HESS sleep states scoring. TSSSAwake Sleep S^(W) S^(N) S^(R) Total HESS Awake S^(W) 2223 1761 401 4385Sleep S^(N) 868 10959 467 12294 S^(R) 289 1341 2340 3970 Total 338014061 3208 20714

TABLE 6 Sensitivity and Positive Predicted Value (PPV) of the HESS.Sleep S^(W) (S^(N) + S^(R)) S^(N) S^(R) Sensitivity (%) 65.77 87.4877.94 72.94 PPV (%) 50.70 92.89 89.14 58.94 Agreement Between TSSS-HESSS^(W) vs 83.78 Kappa = 0.48 (S^(N) + S^(R)) σ = 0.001, z = 61 S^(W) vsS^(N) 75.17% Kappa = 0.54 vs S^(R) σ = 0.001, z = 94 The accuracy of theHESS as computed from contingency table is 82.8%.

TABLE 7 Comparison between percentage of S^(W), S^(N), S^(R) sleepcomputed using TSSS and HESS, across the 23 subjects. Sub S^(W)- S^(W)-S^(N)- S^(N)- S^(R)- S^(R)- Agree- ID TSSS HESS TSSS HESS TSSS HESS mentkappa 1 17 15 70 71 13 24 88 0.64 2 7 14 77 76 16 10 81 0.61 3 37 25 5967 37 26 74 0.59 4 8 7 72 71 20 22 88 0.65 5 14 14 65 60 21 26 81 0.66 614 21 67 58 20 21 83 0.69 7 30 24 61 62 9 14 76 0.56 8 13 9 65 53 22 3776 0.44 9 19 21 65 66 16 13 64 0.49 10 12 16 67 60 21 24 83 0.67 11 1018 76 65 14 17 80 0.57 12 31 19 58 66 11 15 82 0.66 13 7 12 71 78 22 2083 0.65 14 27 30 59 63 14 7 82 0.68 15 7 25 85 70 8 5 72 0.55 16 15 1772 65 13 18 76 0.51 17 19 18 64 38 17 44 61 0.48 18 20 22 66 62 14 16 650.47 19 24 26 66 61 10 13 86 0.71 20 16 19 66 63 18 18 75 0.65 21 13 2075 71 12 9 70 0.52 22 19 21 68 63 13 16 89 0.55 23 9 15 74 58 17 27 710.43 Last two column shows the % agreement and kappa statistic betweenTSSS and HESS.

TABLE 8 Comparison of estimated sleep states computed using singlechannel of EEG at different location over the skull with technicianscored sleep states. Sensitivity PPV TST SE Sleep Wake Sleep WakeAgreement Kappa TSSS 755 73.6 C3 739 72 93 86 95 81 87.5 0.78 C4 750 7392 87 95 85.5 89.5 0.81 Fp1 792 77.2 86.6 49.5 82.7 57.3 75.2 0.56 Fp2792 77.2 87.4 51.3 83.3 59.4 76.4 0.58 F3 741 72.2 89.7 76 91.4 72.684.5 0.73 F4 778 75.8 93 72 90.3 78.7 84 0.71 Fz 770 75 94.2 78.2 92.382.8 87.2 0.78 Cz 782 76.2 86.9 53.5 83.9 59.4 69.4 0.44 T3 875 85.3 9541.7 82 74.8 64.6 0.41 T4 838 81.7 95.5 56.8 86 82 82 0.68 P3 755 73.692.7 79.7 92.7 79.7 86.8 0.77 P4 771 75.2 94.4 78.6 92.5 83.5 84.6 0.74T5 816 79.5 90.9 52 84 67.1 55.1 0.31 T6 860 83.8 95.2 48 83.6 78.3 570.35 O1 789 76.9 90.7 61.6 86.8 70.5 67.2 0.47 O2 854 83.2 94.6 48.383.6 76.2 54.6 0.27 TST—total sleep time, SE—sleep efficiency,PPV—positive predicted value.

TABLE 9 Comparison of Sleep latency computed from SI, that computed bysleep technician using multiple physiological data. Nap 1 Nap 2 Nap 3Nap 4 Subject ID 26 SL - TSSS 7 4 2.5 8.5 SL - SI 5.6 4.3 1.8 6.7Subject ID 27 SL - TSSS 5.5 11.5 6 3 SL - SI 3.9 8.1 3.3 2.8 Sleeplatency was computed in minutes.

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In compliance with the statute, the invention has been described inlanguage more or less specific to structural or methodical features. Theterm “comprises” and its variations, such as “comprising” and “comprisedof” is used throughout in an inclusive sense and not to the exclusion ofany additional features. It is to be understood that the invention isnot limited to specific features shown or described since the meansherein described comprises preferred forms of putting the invention intoeffect. The invention is, therefore, claimed in any of its forms ormodifications within the proper scope of the appended claimsappropriately interpreted by those skilled in the art.

1. A method of determining sleep states from an EEG signal of a subject,the method comprising the steps of: electronically processing the EEGsignal to generate a spectrum of third or higher order of said signal;electronically processing said spectrum to produce at least one spectrumtime series for a predetermined frequency; and electronically processingsaid spectrum time series for compliance with predetermined criteria totangibly classify segments of the EEG signal as corresponding toparticular macro-sleep states of the subject.
 2. A method according toclaim 1, wherein the segments of the EEG signal are tangibly classifiedby indicating their classification on an electronic display for viewingby a user.
 3. A method according to claim 1, wherein the step ofelectronically processing the spectrum to produce at least one spectrumtime series produces a first spectrum time series and a second spectrumtime series for corresponding first and second frequencies.
 4. A methodaccording to claim 1, wherein the spectrum comprises a bispectrum andthe spectrum time series comprises a bispectrum time series.
 5. A methodaccording to claim 4, wherein the step of electronically processing saidbispectrum time series for compliance with predetermined criteriaincludes processing the first bispectrum time series to classify acorresponding one of said segments as Wake or Sleep.
 6. A methodaccording to claim 5, including a step of electronically processing thesecond bispectrum slice time series of said segment classified as Sleepto further classify said segment as NREM or REM sleep.
 7. A methodaccording to claim 4, wherein the step of electronically processing thebispectrum to produce at least one bispectrum time series produces afirst bispectrum time series and a second bispectrum time series forcorresponding first and second frequencies.
 8. A method according toclaim 4, wherein the step of electronically processing said bispectrumtime series against predetermined criteria will preferably includeprocessing the first bispectrum time series to classify a correspondingone of said segments as Wake or Sleep.
 9. A method according to claim 8,including step of electronically processing the second bispectrum timeseries of said segment classified as Sleep to further classify saidsegment as NREM or REM sleep.
 10. A method according to claim 2, whereinthe first frequency falls within a range of 1-8 Hz and the secondfrequency is greater than 9 Hz.
 11. A method according to claim 2,including a step of electronically smoothing the second bispectrum timeseries prior to processing said second bispectrum time series againstpredetermined criteria.
 12. A method according to claim 11, wherein thestep of electronically processing the first bispectrum time seriesagainst predetermined criteria includes comparing values of a segment ofthe first bispectrum time series to a threshold value computed from thefirst bispectrum time series.
 13. A method according to claim 12,wherein the step of electronically processing the second bispectrum timeseries against predetermined criteria includes comparing values of asegment of the second bispectrum time series to a threshold valuecomputed from the second bispectrum time series.
 14. A method accordingto claim 1, including a step of indicating the particular macro-sleepstates of the subject with an electronic display or another sensorysignal modality such as a sound or tactile indication.
 15. A methodaccording to claim 4, wherein the step of generating a bispectrum byelectronically processing the EEG signal comprises an indirectestimation method.
 16. A method according to claim 15, wherein theindirect method includes applying a Fourier transform to cumulant valuescorresponding to the EEG signal.
 17. A method according to claim 16,wherein the step of generating a bispectrum by electronically processingthe EEG signal comprises a direct estimation method.
 18. A methodaccording to claim 3, including a step of electronically processing thefirst spectrum time series to produce an index indicating sleepiness ofthe subject.
 19. A method according to claim 18, wherein the step ofelectronically processing the first bispectrum time series to producesaid index comprises determining a fraction of time over a predeterminedperiod that said series approaches a predetermined threshold value. 20.According to a further aspect of the present invention there is providedan apparatus for detecting sleep states of a subject including: an EEGdigital signal assembly of modules arranged to convert analogue EEGsignals into digital EEG signals; a spectrum assembly responsive to theEEG digital signal assembly and arranged to convert the digital EEGsignals into signals representing spectrum values; a spectrum timeseries assembly in electrical communication with an output side of thespectrum assembly and arranged to generate at least one spectrum timeseries for a predetermined frequency; and a macro-sleep architecture(MSA) assembly responsive to the spectrum time series assembly andarranged to produce classification signals indicating classification ofsegments of the EEG signals into macro-sleep states of the subject. 21.An apparatus according to claim 20, wherein the spectrum assembly is abispectrum assembly that is arranged to convert the digital EEG signalsinto signals representing bispectrum values.
 22. An apparatus accordingto claim 20, wherein the spectrum time series assembly comprises abispectrum time series assembly arranged to generate at least onebispectrum time series for a predetermined frequency.
 23. An apparatusaccording to claim 22, wherein the macro-sleep architecture (MSA)assembly is responsive to the bispectrum time series assembly.
 24. Anapparatus according to claim 20, wherein the EEG digital signal assemblyincludes: EEG electrode ports; an analogue signal conditioning module inelectrical communication with the EEG electrode ports; an analogue todigital converter in electrical communication with the analogue signalconditioning module; and a bandpass digital filter arranged to processoutput from the analogue to digital converter to produce the digital EEGsignals.
 25. An apparatus according to claim 21, wherein the bispectrumassembly comprises: a cumulant calculator; a Fourier transform moduleresponsive to the cumulant calculator to produce a signal representingvalues of a bispectogram; and a bispectrum time series estimatorarranged to produce at least one bispectrum time series signalrepresenting values at a predetermined frequency of a slice of thebispectogram.
 26. An apparatus according to claim 23, wherein the MSAassembly includes a comparator to determine if the value of the at leastone bispectrum time series signal exceeds a predetermined value over asegment.
 27. An apparatus according claim 21, including a sleepinessindex calculator responsive to the bispectrum time series estimator andarranged to generate a signal indicating sleepiness of the subject basedon the bispectrum time series signal for the predetermined frequencyfalling within the EEG Delta band.
 28. An apparatus according to claim20, including a user interface having a display to display a macro sleepstatus determined by the MSA.
 29. An apparatus according to claim 28,wherein the user interface includes a display in communication with thesleepiness index calculator to display the sleepiness index.
 30. Anapparatus according to claim 27 arranged as a driver sleep managementsystem, wherein the sleepiness index calculator is incorporated into asleepiness index alert unit, said alert unit controlling one or more of:a driver intervention module for increasing the wakefulness of thedriver; a wireless communications module for communicating with a basestation; a vehicle control interface for appropriately and safelyimmobilising the vehicle; and a data logger for recording sleepinessindex values of the driver.
 31. A media readable by machine, tangiblyembodying a program of instructions executable by the machine to causethe machine to perform the a method according to claim 1 to determinesleep states from an EEG of a subject.